WW SA Data Warehouse Specialist, WWSO Analytic

Amazon
London
4 weeks ago
Applications closed

Related Jobs

View all jobs

WW SA Data Warehouse Specialist, WWSO Analytic

Job ID: 2850686 | Amazon Web Services Singapore Private Limited

Are you passionate about building Big Data analytics solutions? Amazon Web Services is looking for an experienced Big Data Solutions Architect to work directly with AWS customers to design cloud-based solutions using EMR, Glue, and Data Zone. You will get a chance to work with some of the most prominent startups, as well as some of the largest enterprises, to help them architect scalable Big Data solutions. You will be able to dive deep into the details of customer problems, work with the internal AWS service teams, to create solutions that solve customer needs. You will partner with AWS sales, business development, solutions architecture, and product teams to develop the internal expertise needed to grow analytics-related revenue.


Key job responsibilities:

  1. Collaborate with AWS field sales, pre-sales, training and support teams to help customers learn about and use EMR, Glue, and Spark.
  2. Provide definitive guidance for customers in using EMR and Glue.
  3. Be a trusted collaborator for the EMR/Glue service team as the voice of the customer for defining and prioritizing service features.
  4. Lead enablement by training Solutions Architects, Professional Services Consultants, and Technical Account Managers.
  5. Capture and share best-practice knowledge with customers and the worldwide AWS solutions architect community.
  6. Contribute to the AWS Analytic Blog, service documentation, and reference architectures.
  7. Present at industry events to educate customers and evangelize AWS, EMR, and Glue.


BASIC QUALIFICATIONS

- 8+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience.
- 3+ years of design, implementation, or consulting in applications and infrastructures experience.
- 10+ years of IT development or implementation/consulting in the software or Internet industries experience.

PREFERRED QUALIFICATIONS

- 5+ years of infrastructure architecture, database architecture and networking experience.
- 5+ years of design, implementation, or consulting in applications and infrastructures experience.
- Experience working with end user or developer communities.
- Experience in a technical role within a sales organization.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Careers

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Careers

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.